Description Usage Arguments Details Value Author(s) References See Also Examples

DRW-GM is a disease classification method which performs pathway-based classifier construction and precise disease status prediction by joint analysis of genomic and metabolomic data and pathway topology.

1 2 3 4 5 |

`xG` |
a p x n matrix of gene expression measurements with p genes and n samples. |

`yG.class1` |
a integer vector comprising the indexes of class 1 samples in |

`yG.class2` |
a integer vector comprising the indexes of class 2 samples in |

`xM` |
a m x n matrix of metabolite expression measurements with m metabolites and n samples. |

`yM.class1` |
a integer vector comprising the indexes of class 1 samples in |

`yM.class2` |
a integer vector comprising the indexes of class 2 samples in |

`DEBUG` |
Logical. Should debugging information be plotted. |

`pathSet` |
A list of pathways. Each pathway is represented as a vector of pathway member genes and metabolites. |

`globalGraph` |
An |

`testStatistic` |
The test method used to identify differential genes. For |

`classifier` |
The method to train classifiers. The default is "Logistic". To use other methods, such as "libsvm", one should install the corresponding package in Weka. |

`normalize` |
Logical flag for |

`nFolds` |
The number of folds to split |

`numTops` |
The number of pathway features used for feature selection. Default is 50. |

`iter` |
The number of runs to split |

`Gamma` |
A numeric value. The restart probability in directed random walk. Default is 0.7. |

`Alpha` |
A proportional coefficient to balance the initial weights of genes and metabolites, which are used to construct the initial weights W0 for directed random walk. |

`fdr.output` |
(Approximate) False Discovery Rate cutoff for output in significant genes table. Default is 0.2. |

DRW-GM uses directed random walk to evaluate the topological importance of each gene in reconstructed gene-metabolite graph through integrating information from matched gene expression profiles and metabolomic profiles. The topological importance of genes are used to weight the genes for inferring robust DRW-GM-based pathway activities. Then the pathway activities are selected to train the classifier.

Fitted `"DRWPClassGM"`

model object.

`model` |
Fitted |

`AUC` |
The performance (AUC) of the classifier on |

`Accuracy` |
The performance (Accuracy) of the classifier on |

`pathFeatures` |
The selected pathway features to build the classifier. |

`geneFeatures` |
The genes used to infer the pathways in |

`tScore` |
The t statistic and p-value of each gene in |

`vertexWeight` |
The topological weights of vertexes in |

`pathSet` |
The pathways used to construct the global directed gene-metabolite graph. |

`globalGraph` |
An |

`testStatistic` |
The test method used to identify differential genes. |

`classifier` |
The method to train classifiers. |

`nFolds` |
The number of folds to split |

`numTops` |
The number of pathway features used for feature selection. |

`iter` |
The number of runs to split |

`Gamma` |
The restart probability in directed random walk. |

`Alpha` |
The proportional coefficient to balance the initial weights of genes and metabolites. |

Wei Liu

Liu, W., et al., Topologically inferring risk-active pathways toward precise cancer classification by directed random walk. Bioinformatics, 2013. 29(17): p. 2169-77.

1 2 3 4 5 6 7 8 | ```
data(GProf8511)
data(MProf)
data(pathSet)
data(dGMGraph)
fit <- fit.DRWPClassGM(xG=GProf8511$mRNA_matrix, yG.class1=GProf8511$normal, yG.class2=GProf8511$PCA,
xM=MProf$Meta_matrix, yM.class1=MProf$normal, yM.class2=MProf$PCA, DEBUG=TRUE,
pathSet=pathSet, globalGraph=dGMGraph, testStatistic="t-test", classifier = "Logistic",
normalize = TRUE, nFolds = 5, numTops=50, iter = 1, Gamma=0.7, Alpha = 0.5)
``` |

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